My "Data Analysis and Visualization in Python" repository is a compilation of coursework from my studies at the FIT faculty. This repository consists of three main folders, each dedicated to a specific aspect of data analysis:
- Download (Parsing): In this section, I showcase my proficiency in data acquisition and parsing. I have tackled various tasks related to web scraping and data collection, employing tools like BeautifulSoup to extract structured data from websites.
- Data Preparation: This folder highlights my skills in data cleaning, preprocessing, and transformation. I utilize libraries such as NumPy and Pandas to efficiently prepare and organize data for analysis and visualization.
- Data Visualization: In this section, I demonstrate my ability to create informative and visually appealing data visualizations. I leverage libraries like Seaborn to generate insightful charts, graphs, and plots that facilitate data interpretation.
Throughout this repository, you'll find examples of my work with essential data analysis and machine learning libraries like Scikit-learn. These tasks encompass a range of techniques, from basic data retrieval to advanced machine learning applications.
My "Data Analysis and Visualization in Python" repository reflects my dedication to mastering the tools and techniques necessary for effective data analysis and visualization in the Python ecosystem.